AIMC Topic: Immunohistochemistry
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An open-source semi-automated robotics pipeline for embryo immunohistochemistry.
Scientific reports
May 13, 2021
A significant challenge for developmental systems biology is balancing throughput with controlled conditions that minimize experimental artifacts. Large-scale developmental screens such as unbiased mutagenesis surveys have been limited in their appli...
Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer.
Scientific reports
Mar 22, 2021
This study addresses the core issue facing a surgical team during breast cancer surgery: quantitative prediction of tumor likelihood including estimates of prediction error. We have previously reported that a molecular probe, Laser Raman spectroscopy...
Diagnosis prediction of tumours of unknown origin using ImmunoGenius, a machine learning-based expert system for immunohistochemistry profile interpretation.
Diagnostic pathology
Mar 11, 2021
BACKGROUND: Immunohistochemistry (IHC) remains the gold standard for the diagnosis of pathological diseases. This technique has been supporting pathologists in making precise decisions regarding differential diagnosis and subtyping, and in creating p...
Classification of colorectal tissue images from high throughput tissue microarrays by ensemble deep learning methods.
Scientific reports
Jan 27, 2021
Tissue microarray (TMA) core images are a treasure trove for artificial intelligence applications. However, a common problem of TMAs is multiple sectioning, which can change the content of the intended tissue core and requires re-labelling. Here, we ...
LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer.
Cancer research and treatment
Dec 29, 2020
PURPOSE: The role of tumor-infiltrating lymphocytes (TILs) in predicting lymph node metastasis (LNM) in patients with T1 colorectal cancer (CRC) remains unclear. Furthermore, clinical utility of a machine learning-based approach has not been widely s...
Automatic discovery of clinically interpretable imaging biomarkers for Mycobacterium tuberculosis supersusceptibility using deep learning.
EBioMedicine
Nov 7, 2020
BACKGROUND: Identifying which individuals will develop tuberculosis (TB) remains an unresolved problem due to few animal models and computational approaches that effectively address its heterogeneity. To meet these shortcomings, we show that Diversit...
The role of artificial intelligence to quantify the tumour-stroma ratio for survival in colorectal cancer.
EBioMedicine
Oct 21, 2020
Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer.
EBioMedicine
Oct 8, 2020
BACKGROUND: An artificial intelligence method could accelerate the clinical implementation of tumour-stroma ratio (TSR), which has prognostic relevance in colorectal cancer (CRC). We, therefore, developed a deep learning model for the fully automated...
Artificial intelligence to detect MYC translocation in slides of diffuse large B-cell lymphoma.
Virchows Archiv : an international journal of pathology
Sep 26, 2020
In patients with suspected lymphoma, the tissue biopsy provides lymphoma confirmation, classification, and prognostic factors, including genetic changes. We developed a deep learning algorithm to detect MYC rearrangement in scanned histological slide...
Staining and Labeling
Predictive Value of Tests
Humans
Microscopy
Phenotype
In Situ Hybridization, Fluorescence
Image Interpretation, Computer-Assisted
Reproducibility of Results
Proto-Oncogene Proteins c-myc
Biomarkers, Tumor
Immunohistochemistry
Lymphoma, Large B-Cell, Diffuse
Translocation, Genetic
Proof of Concept Study
Deep Learning
Antigens, CD20
Genetic Predisposition to Disease
Deep learning quantification of percent steatosis in donor liver biopsy frozen sections.
EBioMedicine
Sep 24, 2020
BACKGROUND: Pathologist evaluation of donor liver biopsies provides information for accepting or discarding potential donor livers. Due to the urgent nature of the decision process, this is regularly performed using frozen sectioning at the time of b...
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